Quality assessment of media data, e.g. graphics, still images, videos or audio files, is useful for evaluation and/or control of recording equipment, compression methods or transmission channels. It can be further used to monetize media content differently in dependency on the media's quality.
Most precise and direct way for assessing video quality is subjective quality score assignment. But, subjective assignment is expensive and time-consuming. Thus, objective video quality measurement (VQM) has been proposed as an alternative method, in which it is expected to provide a calculated score as close as possible to the average subjective score assigned by subjects.
In so called non-reference methods where no source media data information is available for VQM, mapping between objectively detectable features such as artefact features and the prediction of subjective scores is crucial. There is a bouquet of methods in the art for establishing such mapping. For instance, Artificial Neural Networks (ANN) are trained to predict mean observer scores (MOS) from objectively detectible artefact features. Although artificial neural networks achieve good results for test data in problems where training and test data are related to similar content, it is not easy to achieve stable performance when extending to wide range of contents.
Further, there are semi-supervised learning methods in which a small quantity of labelled and a large number of unlabeled data can be involved into training together to achieve better performance.
Due to the complexity of these underlying techniques, use of current video quality assessment (VQA) techniques, as described in unpublished PCT-Applications PCT/CN2010/000600 and PCT/CN2010/001630 for instance, has been restricted to professional customers due to high computing costs and correspondingly high expenses.
But individual media production and consumption becomes more and more popular. That is, customers can capture, process, compress, access, and share media content like music, audio takes, images and videos anywhere and anytime.
The more amateur and semi-professional users spread there content the more they are interested in becoming enabled to assess the quality of their media data just the way professionals do it.
However, cost of professional VQA is still too high for amateur.